Search Results (348)

This study investigates the capabilities of modern ultrasonic non-destructive testing (NDT) methods for the detection and sizing of fatigue cracks in hinge bolts used in railway transport. Experimental investigations were conducted using immersion ultrasonic testing combined with advanced signal processing techniques, including PAUT, FMC/TFM, and FMC/PCI. Artificially introduced erosion notches ranging in size from 0.025 mm to 23 mm were used to simulate fatigue defects. The probability of defect detection was evaluated through Hit/Miss analysis and determination of the a90/95 parameter. The results indicate that the FMC/TFM method provides the highest sensitivity for the detection of small defects, while the PAUT technique demonstrates the highest accuracy in defect sizing. Full article

Gas-insulated switchgears (GISs) and gas-insulated transmission lines (GILs) are essential for large-capacity power transmission in demanding environments, such as high drops, large spans, and heavy pollution. As the core components providing both electrical insulation and mechanical support, ultra-high voltage (UHV) epoxy-based insulators often suffer from high internal residual stress. This issue, compounded by a lack of reliable detection methods, frequently results in equipment being commissioned with hidden defects. To address this, this review first examines the formation mechanisms of curing deformation and residual stress in oversized insulators based on cure kinetics and thermo-chemical coupling models. Subsequently, it provides a comprehensive summary of current residual stress measurement techniques, comparing the applicability and limitations of embedded sensors, direct mechanical measurements, and indirect non-destructive testing (NDT) methods. Finally, by coupling residual stress with filler sedimentation, the stress distribution patterns and mechanical reliability of epoxy-based insulators across different life-cycle stages are analyzed. These insights offer valuable theoretical references for the structural design, process optimization, and performance evaluation of oversized epoxy-based insulators, ultimately contributing to the intrinsic safety of UHV power equipment. Full article

Hybrid imaging methods are emerging as one of the most dynamically evolving research areas in industrial inspection systems. This paper presents a literature review covering relevant scientific publications and official reports on the use of multimodal approaches in quality inspection and NDT systems. Hybrid imaging involves combining two or more imaging techniques to enhance the detection, characterization, and interpretation of features in inspected objects. The paper describes the physical foundations of vision-based inspection systems, including the interaction of optical radiation with matter. It also introduces a classification of optical methods and discusses the role of image fusion in multimodal data processing, with particular emphasis on high-speed quality control systems. The review outlines the current capabilities, limitations, and industrial applications of hybrid imaging, as well as future research directions, including integration with real-time systems and the use of artificial intelligence for automated defect interpretation. Full article

Structural health monitoring (SHM) systems fundamentally rely on effective sensing technologies for reliable damage detection and structural condition assessment. Among the available sensing approaches, piezoelectric (PZT)-based transducers are widely used in civil engineering due to their dual actuation–sensing capability, high sensitivity, low cost, and suitability for real-time monitoring. However, SHM performance not only depends on the sensing hardware, but also on the signal processing techniques that extract meaningful damage-related information from measured responses. Recently, Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown strong potential to enhance automation and improve the performance of SHM systems. This paper provides a critical review of signal processing and data-driven learning approaches for PZT-based guided-wave (GW) SHM and nondestructive testing (NDT), with applications to metallic, composite, and concrete structures. The review covers developments from early ML-based GW SHM methods to recent advances in DL, hybrid frameworks, and physics-informed approaches. Although emphasis is placed on civil infrastructure, developments in other fields such as aerospace and energy engineering are also reviewed due to their role in validating GW-based SHM methodologies. The fundamental theory of PZT sensing and guided wave propagation is introduced to establish the required background for monitoring techniques. Classical signal processing methods are then reviewed, followed by AI-based SHM frameworks, with particular emphasis on hybrid approaches that integrate physics-based signal processing with data-driven models to improve robustness, accuracy, and generalization. Key challenges such as environmental variability, sensor degradation, limited labeled data, and model transferability are discussed, along with future research directions including physics-informed machine learning (PIML), transfer learning, explainable AI, and baseline-free SHM. The review highlights that hybrid and physics-informed frameworks offer strong potential for field deployment by improving robustness, reducing data dependency, and enhancing generalization capability. A key contribution of this work is the comparative synthesis of signal processing, ML, DL, and hybrid methodologies across different material systems and structural types, together with a structured discussion of the challenges and future research directions for real-world implementation. Full article

X-ray testing is a cornerstone nondestructive testing (NDT) technique in the nondestructive testing of welds. To address the challenges posed by minute defects such as cracks and pinholes—characterized by small size, weak features, and a tendency to be confused with noise—this paper proposes a minute defect recognition framework based on sparse representation. (1) Median filtering was selected as the basic denoising method. In combination with image enhancement, the discriminability of weld regions and defect features was improved. (2) A segmented ROI extraction method combining Otsu threshold segmentation and Sobel edge detection was proposed. This method can better adapt to inclined or curved weld images and effectively reduce background interference. (3) A micro-defect recognition method based on sparse representation was proposed. By constructing an SDR and combining dictionary learning with sparse solving models, effective representation and classification of micro-defect regions were achieved. Its effectiveness and engineering application value were verified through actual engineering data, third-party witness tests, and competition results. Full article

Chloride and sulfate ion attacks are among the leading causes of deterioration in reinforced concrete structures, leading to the corrosion of steel reinforcement, expansion, cracking, and premature structural failure. Early detection of these ion-induced deteriorations is essential not only for maintaining safety but also for supporting sustainability objectives by extending service life, reducing material consumption, and minimizing carbon-intensive repairs. This review synthesizes current advances in non-destructive testing (NDT) techniques used to identify and quantify the impacts of chloride and sulfate ions in reinforced concrete. The mechanisms of ion ingress and their associated degradation processes are examined together with the operating principles, strengths, and limitations of key NDT methods, including electrical resistivity, acoustic emission, infrared thermography, ground penetrating radar, and ultrasonic pulse velocity. By enabling timely maintenance decisions and reducing unnecessary demolition or intrusive testing, these NDT methods contribute directly to sustainable infrastructure management. Through comparative analysis and real-world case studies, the paper highlights the most effective NDT applications for deterioration scenarios and outlines emerging innovations that enhance accuracy, data interpretation, and long-term monitoring capabilities. The findings demonstrate how advancements in NDT support the development and preservation of durable and sustainable concrete structures. Full article

Reinforced concrete structures, once considered very durable and capable of withstanding a variety of adverse environmental conditions, often suffer from premature reinforcement corrosion, compromising their safety and serviceability. Ensuring the safety of bridges and buildings requires effective, non-destructive inspection and monitoring techniques to assess the state of degradation without damaging the integrity of the asset. Although a wide range of non-destructive testing (NDT) methods is currently available, few are capable of identifying durability issues during the initial stages before the damage becomes critical. To address this gap, this paper describes an innovative laboratory experiment based on an integrated approach that combines Ground-Penetrating Radar (GPR) and electrochemical methods. This research represents an advanced step in our ongoing projects, merging geophysical and electrochemical expertise to enhance diagnostic precision. A reinforced cement mortar specimen was subjected to free corrosion via partial immersion in sodium chloride solutions of varying concentrations (1, 10, and 35 g/L), followed by an accelerated corrosion phase. The phenomenon was monitored simultaneously using GPR and electrochemical tests. Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. Specifically, the application of the Hilbert Transform to GPR signals allowed for a correlation between envelope amplitude variations and the electrochemical behavior of the rebars. These laboratory results highlighted that an integrated observation was useful to indirectly observe the evolution of the phenomenon of corrosion in the steel reinforcement embedded in the mortar specimens. Full article

The Garisenda Tower, along with the neighboring Asinelli Tower, is arguably the symbol of the city of Bologna. They are the sole remnants of about one hundred towers that formed the city’s skyline in medieval times. As such, the monitoring of their state of health has been of great interest to the scientific community for more than a century—one example being the studies of Prof. Cavani in the early 1900s. The Garisenda Tower, famous for its impressive lean, is the object of Structural Health Monitoring (SHM) involving a multitude of devices. Some examples are a 30 m long pendulum installed on the inside of the tower to measure the planar displacement of the tower’s top; Fiber-Optical Strings (FOSs) installed in the walls of the basement to measure their vertical deformation; and piezoelectric acoustic emission (AE) sensors, also installed on the walls of the tower’s basement to detect elastic waves generated by micro-cracking. This rich experimental setup allows for the investigation of the tower’s stability and damage assessment. In this work, attention is focused on two analyses: The first is a Principal Component Analysis (PCA) study that investigates the correlation between AE data and other SHM data, such as in situ temperature, pendulum displacement, and AE rate. The second analysis corresponds with numerical finite element (FE) studies that assess damage in the base of the tower. Initially, the Smeared Cracking material model is used to understand which zones of the tower are more damaged. Moreover, a possible critical scenario due to increasing tower tilt is investigated. Finally, a viscoelastic formulation of the materials at the base of the tower is used to account for creep to understand the possible viscous effects at the base of the tower. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

As critical components of wind energy systems, the structural integrity of wind turbine blades is directly tied to the operational safety and economic performance of wind turbines. With blade designs trending toward larger and more flexible structures and operating environments becoming increasingly harsh, maintenance strategies must urgently shift from reactive approaches to predictive maintenance paradigms. From an engineering application perspective, this study conducts a systematic and critical review of non-destructive testing (NDT) and structural health monitoring (SHM) technologies for wind turbine blades. Drawing on the literature published over the past decade, we examine the field applicability, limitations, and engineering challenges of core NDT techniques—including vision-based methods, acoustic approaches, vibration analysis, ultrasound, and infrared thermography. Particular emphasis is placed on the integration of data-driven approaches with engineering practice, evaluating the role of machine learning in fault classification and anomaly diagnosis, as well as the contributions of deep learning to automated defect detection in image and signal data. Moreover, this paper critically discusses the growing use of robotic inspection platforms, such as unmanned aerial vehicles and climbing robots, as multi-sensor carriers enabling rapid and comprehensive blade assessment. By comparatively analyzing detection performance, cost, and automation levels across technologies, we identify key engineering barriers, including environmental noise robustness, signal attenuation within complex blade structures, and the persistent gap between laboratory methods and field deployment. Finally, we outline forward-looking research directions, encompassing multi-modal sensor fusion, edge computing for real-time diagnostics, and the development of standardized SHM systems aimed at supporting full lifecycle blade management. Full article

Steel plate structural integrity is vital for infrastructure and industrial applications, but surface/subsurface defects severely reduce component reliability. Conventional NDT techniques have limitations: Ultrasonic testing is sensitive to surface roughness, eddy current testing lacks deep flaw sensitivity, and vision-based methods are affected by illumination. Although MOI is promising for defect inspection, it faces noisy/low-contrast images and poor feature extraction; existing deep learning models struggle to balance accuracy and real-time performance. Herein, a real-time MOI defect detection framework with deep feature fusion under alternating magnetic excitation is proposed. It uses Pix2Pix cGAN for data augmentation, integrates S-GhostConv for efficient feature extraction, and adopts an improved PANet with attention mechanisms for multiscale fusion. Experiments on real and 6000 synthetic MOI images (four defect types) show it achieves 0.990 mAP0.5 and 130 FPS, outperforming YOLOv8s by 7.1% in accuracy. This framework provides a reliable solution for industrial steel plate defect inspection with broad application prospects. Full article

Non-destructive testing (NDT) methods are widely used to evaluate the performance of concrete, but their accuracy can be influenced by external factors such as curing temperature. Temperature not only modifies hydration kinetics and strength development but may also change the correlation between NDT measurements and compressive strength. However, no prior research has systematically examined how different curing temperatures influence the reliability of various NDT techniques. This study evaluates three curing temperatures and their effect on the correlation between NDTs and compressive strength at various ages (1, 3, 7, 28, and 90 days). Both simple regression analysis and artificial neural networks (ANNs) were employed to predict strength from NDT measurements. Results show that NDT sensitivity to curing temperature is most pronounced at early ages, and that linear regression models cannot adequately capture the complexity of these relationships. In contrast, ANNs demonstrated superior predictive capability, though initial training with limited data led to overfitting and instability. By applying Gaussian Noise Augmentation (GNA), model accuracy and generalization improved substantially, achieving R2 values above 0.95 across training, validation, and test sets. These findings highlight the potential of non-linear models, supported by data augmentation, to improve prediction reliability, lower experimental costs, and more accurately capture the role of curing temperature in NDT–strength correlations for concrete. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

In this paper, the combined use of Ground Penetrating Radar (GPR) and a Terrestrial Laser Scanner (TLS) is illustrated to highlight multiple advantages arising from the integration of these two distinct Non-Destructive Testing (NDT) techniques in the investigation of a historical wall. In particular, thanks to the TLS point cloud, a precise evaluation of the medium’s thickness, as well as its irregularities, was carried out. Based on this accurate geometrical constraint, a first-order velocity model, to be used for a time-to-depth conversion and for a post-stack GPR data migration, was computed. Moreover, a joint visualization of both datasets (GPR and TLS) was achieved in a novel tridimensional workspace. This solution provided a more straightforward and efficient way of testing the reliability of the combined results, proving the efficiency of the proposed method in the estimation of a velocity model, especially in comparison to conventional GPR methods. This demonstrates how the integration of different remote sensing methodologies can yield a more solid interpretation, taking into account the uncertainties related to the geometrical irregularities of the external wall’s surface and the inner structure generating complex GPR signatures. Full article